Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence
DOI
https://doi.org/10.25772/HKBB-M048
Defense Date
2005
Document Type
Thesis
Degree Name
Master of Science
Department
Computer Science
First Advisor
Dr. Lorraine M. Parker
Abstract
Fuzzy attributes are used to quantify imprecise data that model real world objects. To effectively use fuzzy attributes, a fuzzy membership function must be defined to provide the boundaries for the fuzzy data. The initialization of these membership function values should allow the data to converge to a stable membership value in the shortest time possible. The paper compares three initialization methods, Random, Midpoint and Random Proportional, to determine which method optimizes convergence. The comparison experiments suggest the use of the Random Proportional method.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
June 2008